An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning

Abstract

We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read. The agent is expected to learn to follow the narrations, thus disambiguating mentions and discovering new individuals. We focus on the case in which individuals are entities and relations and propose an end-to-end trainable memory network that learns to discover and disambiguate them in an online manner, performing one-shot learning and dealing with a small number of sparse supervisions. Our system builds a not-given-in-advance knowledge base, and it improves its skills while reading the unsupervised text. The model deals with abrupt changes in the narration, considering their effects when resolving coreferences. We showcase the strong disambiguation and discovery skills of our model on a corpus of Wikipedia documents and on a newly introduced data set that we make publicly available.